> For the complete documentation index, see [llms.txt](https://dots.gitbook.io/dots-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dots.gitbook.io/dots-docs/demos/educational-demo.md).

# Educational Demo

## Brief overview of this case study

### About the dataset

{% hint style="info" %}
For demo purposes only, this dataset is synthetic and designed to represent a realistic example of how such data would appear in a real-world context.
{% endhint %}

This dataset represents a rich, multi-layered view of how classroom practices evolve within a large-scale education program. It brings together both qualitative and quantitative information to understand change over time—not just in terms of outcomes, but in terms of lived experiences inside classrooms.

At its core, the data captures three interconnected perspectives:

* **Teachers** — through baseline and ongoing reflections, adaptation narratives, and observed classroom practices. This shows how teachers start, what changes in their approach, and how they adjust practices based on real-world constraints.
* **Classrooms** — through structured observation scores and descriptive notes, offering both measurable indicators of teaching practices and contextual detail about what is actually happening during lessons.
* **Students** — through summarized interview data and engagement indicators, reflecting how classroom experiences are perceived and whether changes in teaching translate into different learning environments.

The dataset is **longitudinal**, meaning it tracks the same teachers and classrooms over time (baseline, midline, endline), allowing for analysis of progression, consistency, and change. It is also **relational**, with different data sources linked through common identifiers like school, district, and teacher, enabling cross-comparison and triangulation.

A key characteristic of this data is its **blend of structured metrics and narrative insights**. Numeric scores provide patterns and trends at scale, while qualitative inputs—like teacher reflections and facilitator notes—explain the “why” behind those patterns. This makes it possible to move beyond surface-level analysis and understand variation across contexts, such as why certain practices are adopted differently in different classrooms or regions.

Overall, the dataset is designed to support a nuanced understanding of:

* How teaching practices shift over time
* How and why those practices are adapted in real contexts
* How students experience these changes in the classroom

Rather than isolating variables, the data emphasizes **connections between behavior, context, and experience**, making it well-suited for deep, explanatory analysis across a complex education system.<br>

## Exploring on the platform

Once you log in, you can explore the datasets already available on the platform. These datasets allow you to try out and experience the Analysis features, helping you understand how different tools and functionalities work without needing to upload your own data.\
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We recommend using the **Classroom Practice & Mastery Observations** dataset to explore the platform’s quantitative analysis tools, and the **Student Experience Interviews** dataset to try out the qualitative analysis tools.

**Dataset View - Document, Responses and Overview**\
Datasets can be viewed in multiple formats, with breakdowns at different levels. Learn more about these views below.

* **Document Level** - [Read More](https://dots.gitbook.io/dots-docs/analysis-features/document-wise)

The Document-wise Analysis feature lets you view all responses for a template in Table, Card, or List views, allowing you to switch views easily and explore the data in the format that suits you best.

* **Response Level** - [Read More](https://dots.gitbook.io/dots-docs/analysis-features/responses)

The Responses feature lets you view all responses for a template in both Listing and Graphical views, with options to filter fields as needed, and provides summaries for qualitative inputs to help quickly understand key insights.

* **Overview Dashboard** - [Read More](https://dots.gitbook.io/dots-docs/analysis-features/overview-dashboard)

The Overview Dashboard helps to manage/view and analyze qualitative and quantitative responses. It provides a real-time summary of collected data in multiple formats, graphical charts, individual response view, and an option to export data for further analysis.

**Themes Manager and Annotations**&#x20;

* **Highlights Interface** - [Read More](https://dots.gitbook.io/dots-docs/highlights-and-themes-manager/highlights-interface)

Quickly navigate all highlighted fragments instead of manually browsing documents, saving time and reducing missed insights. Filter by any dataset attribute and  use summarization to get the gist of annotations.

**Analysis - Ask AI, Reports & Pattern Insights**<br>

* **Ask AI** - [Read More](https://dots.gitbook.io/dots-docs/analysis-features/ask-ai)

Your natural-language assistant for exploring and analyzing your full dataset (qual + quant) through conversational Q\&A. Quickly get contextual insights without writing queries or code, and apply filters or select datasets to generate accurate summaries.

* **Reports** - [Read More](https://dots.gitbook.io/dots-docs/analysis-features/reports)

&#x20;Bring together qualitative and quantitative data into a coherent narrative or analytical view. Select and compare data points to uncover relationships, patterns, and insights, making it ideal for analysis, storytelling, and sharing findings with stakeholders.

* **Pattern Insights Dashboard** - [Read More](https://dots.gitbook.io/dots-docs/analysis-features/pattern-discovery)

Automatically analyzes your annotation tags to reveal meaningful patterns, co-occurrences, and relationships in qualitative data. AI-generated summaries help uncover key insights quickly without manual analysis.

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